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Tuesday's match between Greece and the Czech Republic has huge implications poland greece betting preview goal both sides poland greece betting preview goal terms of their chances to advance to the knockout stages of the European Championship. Greece was able to come away with a draw in its opening match against Poland in Warsaw, despite playing the majority of the match a man down. Poland greece betting preview goal Polish side looked in control, and it seemed they would start the tournament off with a win, but substitute Dimitris Salpingidis found the back of the net in the 51st minute to tie the game at one. The Czech Republic was taken behind the woodshed by Russia in its first match, falling in embarrassing fashion They'll certainly need a better effort if they want to avoid being all but mathematically eliminated after their first two games. Sokratis Papastathopoulos is suspended for this one after being sent off against Poland on a controversial call, to say the least. Avraam Papadopoulos, another key defensive contributor for the Greeks, is out of the tournament with a knee injury.

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Our MVPF estimates measure the welfare impact of a range of government policies. Here, we show how one can use the MVPF framework to understand the value of future research that reduces the uncertainty in our estimates. The MVPF framework provides a measure of the value of information because it is a price: it measures the price faced by the government to redistribute across beneficiaries of different types of policies.

A welfare-maximizing government should be willing to pay to reduce the uncertainty in these prices, just as consumers would be willing to pay to learn the true value of the products they buy. There are many ways one could conceptualize reducing the various sources of modeling and sampling uncertainty in our estimates.

In this section, we develop a simple approach to measure the value of reducing sampling uncertainty. We defer an exhaustive treatment to future work. We use this example to illustrate the value of future research that increases estimate precision, perhaps through improved access to larger administrative longitudinal data sets. The policy has a net cost to the government of G j and a willingness to pay of WTP j per dollar of programmatic cost.

The policy maker does not know the true values of WTP j and G j. We estimate the value of info in equation 9 both for each individual policy and for our category averages. Broadly, we find the highest values of future research for policies with uncertain long-run effects on children. Moreover, we also find large values of information for policies with potential indirect effects on children and uncertain effects on adults.

We also find large values of information for college subsidies to parents shown in green; color version available online. This reflects the fact that these policies have highly uncertain effects on college attainment, and small increases in attainment can translate into large gains. In contrast, we find smaller values of information for policies where the effects have been already precisely estimated. For example, we find the evidence-based policy maker would be willing to pay little to remove the statistical uncertainty in the estimated impact of disability insurance on labor earnings e.

This lower value of information reflects the relatively high precision of existing estimates in those studies. In practice, the true effect is never observable. That said, improved access to larger administrative data sets can help obtain more precise effects of government policies. For example, a policy maker can decide whether a researcher should use a survey data set for the analysis or obtain access to linked administrative data on the population.

This yields an infinite point estimate for our MVPF, and we find a willingness to pay estimate of 6. This exercise illustrates that if the policy maker only knew the PSID estimates, there would be a large value in learning additional information before making this investment decision. This is, of course, a stylized exercise. We are imagining a policy maker that sees the ex post evaluation of a policy prior to making her decision—something that is clearly not feasible.

The goal here is merely to illustrate potential value of expanding access to administrative data sets that can generate more precise estimates of long-run policy effects. While we focus on computing the MVPF for each policy, the most common form of welfare analysis in previous literature is benefit-cost analysis, as in equation 4. With that in mind, we compare our results to the benefit-cost ratios for the same policies.

Our general conclusion about the high returns to investment in children would remain true even if one used a benefit-cost ratio instead of the MVPF. The average benefit-cost ratio is 4. In contrast, we find smaller benefit-cost ratios for adult policies—often less than 1. This figure presents estimates of benefit-cost ratios for all policies evaluated in the article and shows their relationship to the MVPF.

The method for calculating these benefit-cost ratios is outlined in Section II. Panel A plots the benefit-cost ratio of each policy as a function of the age of the beneficiaries, along with category average estimates and their confidence intervals. Panel B plots the benefit-cost ratio of each policy as a function of the MVPF estimate for the policy. Panels A and B present a decomposition of the elements that make up our income projection process for the examples in Section III.

This number is constructed by assuming that the mean wage level at each age will rise and has previously risen by 0. See Online Appendix I for further details of this methodology. This figure presents estimates of WTP normalized by initial programmatic spending for each category-average group of policies in our baseline sample. The normalized willingness to pay of individual policies are shown in smaller dots, color-coded to align with their respective categories.

This figure assesses the impact of observing child effects on our estimates as a function of the average age of the economic beneficiaries of the policy. Panel A restricts our sample to the subset of policies for which we observe estimates of the impact of the policy on children. In addition, Panel B shows projected MVPFs for additional policies that do not observe earnings impacts but do observe another intermediate outcome such as birthweight AFDC , college attendance housing vouchers to AFDC recipients , and test scores housing vouchers in Chicago.

Panel B differs slightly from our baseline specification because we restrict to the subset of policies for which we are able to vary the discount rate e. We omit confidence intervals for ease of viewing, but caution the reader that the estimate for the college adult category has a CI that includes 0 and infinity.

Panel A replicates our baseline specification using the CBO estimates of the tax rates. This figure presents the category-average MVPFs from Figure III using a range of different alternative specifications that are more conservative than our baseline specifications. Panel B replaces our baseline income projection procedure with a procedure that assumes zero income growth over the lifecycle.

We use our restricted sample of policies for this specification. See Online Appendix I for further details. Panel B restricts the sample to only policies for which earnings outcomes are estimated for at least five years of follow-up after the policy. For this panel we show group averages even for groups with a single policy.

Panel C restricts the sample to policies whose identification strategy is a randomized control trial, lottery, or regression discontinuity. Panel D restricts to policies whose primary analyses have been published in a peer-reviewed journal. We do not report confidence intervals for these estimates to our knowledge there is no well-accepted method of constructing such intervals ; but we refer readers to Figure IV , Panel B to note that some of these category averages are imprecise.

This figure presents MVPFs for all policies evaluated in the article based on the year in which the policy was implemented. Policies are divided into categories based on their decade of implementation and the average age of their economic beneficiaries. For policies implemented in each decade there are two categories— policies with beneficiaries over age 23 and policies with beneficiaries aged 23 or younger. This is the same approach used to create MVPF estimates for policy domains in previous figures.

We report MVPF estimates using three potential measures of WTP: i cost, the mechanical cost of the program incurred by the government, excluding any fiscal externalities from behavior change. Estimates from this specification are denoted by circles. Estimates from this specification are denoted by Xs. Estimates from this specification are denoted by triangles. In general, we find a fairly monotonic relationship—policies with high benefit-cost ratios also have high MVPFs. There are, however, some notable distinctions.

Similarly, the top tax rate reduction has an infinite MVPF but a benefit-cost ratio of 1. By the standards of benefit-cost ratios, these policies may not appear all that desirable, even though the MVPF point estimates imply that they pay for themselves and provide a Pareto improvement.

The difference between the MVPF and benefit-cost ratio in these cases reflects the fact that the benefit-cost ratio places all causal effects of the program in the numerator while the MVPF incorporates effects based on their incidence. In particular, the numerator of the MVPF captures the effects on beneficiaries while the denominator captures all effects on the government budget.

In measuring the welfare effects of the Medicaid expansion and the tax cut, MVPF places all fiscal externalities in the denominator. The results show us that these policies have substantial benefits and limited or no net government cost.

In the benefit-cost ratio framework these reforms would have been interpreted as high-cost policies with substantial benefits. The second crucial distinction between the MVPF and benefit-cost ratio is how the two approaches conceptually close the budget constraint. In many cases, benefit-cost analysis includes no discussion of closing the budget constraint.

In cases where the concept is addressed, it is customary to close the budget constraint in the same manner regardless of the policy context. To see how this matters, consider the tax reform that simultaneously raised top marginal income tax rates and expanded the EITC. One could, in principle, use a benefit-cost ratio to evaluate whether the EITC expansion was desirable. The costs exceed the benefits and so, if the government were applying a strict benefit-cost test, we would not expect this policy to be implemented.

That said, the goal of the EITC expansion was to provide redistributive benefits to low-income workers. Its MVPF is 1. As noted, the MVPF point estimate for the top tax rate change is 1. Throughout, we aimed to include all possible MVPFs in the categories we considered.

We included any policy where we thought we could provide reasonable measures of both costs and WTP. One set of notable omissions are the state-level welfare reforms made by states that sought to increase family self-sufficiency. Throughout the s and early s, states experimented with a range of reforms to cash welfare programs that imposed term limits, provided job training and other educational services, and provided job search and placement assistance.

The omission of these reforms is not because they were not analyzed. Many states rigorously evaluated the effect of these reforms. These RCTs measured and provided a clear estimate of the net cost of each reform. However, the design of the policies enacted in each state makes it difficult to understand their welfare effects. Generally, programs contained both a carrot and a stick. It may be that government revenue gains were the result of expanded job opportunities due to program participation.

In that case, willingness to pay would be positive. By contrast, it may be that the government revenue gains were the result of stricter attendance requirements that drove individuals off welfare. In that case, willingness to pay would be negative.

This highlights the value of isolating the carrot and the stick into separate RCTs. In Appendix Figure X , we conduct a range of bounding exercises that attempt to construct lower and upper bounds on WTP for these welfare reform programs. Unfortunately, the bounds are very wide. In this article, we examine the MVPF of different historical policies over the past half-century in the United States.

We find a clear and persistent pattern that direct investments in children have yielded the largest MVPFs. We also demonstrate that in a meaningful number of cases, these policies pay for themselves. From a taxpayer perspective, these expenditures on children are investments, rather than just transfers. We find that opportunities for high-return investments in children have persisted across policy categories for many decades.

This is, however, no guarantee that all future investment in these categories will produce high MVPFs. Indeed, we find that MVPFs vary substantially within policy categories. Low-return policies exist even in high-return categories. This highlights the value of further understanding the mechanisms behind the high MVPFs of successful historical investments.

Even in cases where there is existing research, much remains unknown about the welfare consequences of government policy. To that aim, we quantify the value of future work that uses new data to reduce estimate uncertainty.

We show that in many cases, an evidence-based policy maker seeking to maximize social welfare should be willing to make substantial budgetary expenditures to learn more about policy effectiveness. In particular, our results highlight the value of expanded use of administrative data for policy analysis. The policies included in this article are just a small subset of those that could be analyzed using the MVPF. We do not discuss the MVPF of crime policies, environmental policies, macroeconomic stabilization policies, or infrastructure policies, among many others.

With careful tracking of willingness to pay and net costs, the MVPF can be used in any of these contexts and can guide cost-benefit analyses. We leave that analysis for future work. We first and foremost thank the several hundred researchers whose empirical results form the foundation of our estimates.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the National Science Foundation. See Mayshar , Slemrod and Yitzhaki , , and Kleven and Kreiner for original definitions, and Hendren for a comparison of the MVPF to alternative measures of welfare. In several cases where authors constructed their own MVPFs, we incorporate those estimates directly.

Where applicable, we adjust these estimates to harmonize assumptions e. In cases where previous literature has conducted comprehensive cost-benefit analyses of a policy, we draw on the components of those analyses to reformulate them into their implied MVPF. The intuition here comes from the envelope theorem. Additional costs due to behavioral responses are not valued dollar for dollar.

To align with terminology in existing literature, we use various terms interchangeably to refer to the same phenomenon. Any policy with a positive willingness to pay and negative net costs we define to have an infinite MVPF. This is because the expenditure is valued by beneficiaries and has no net cost on the government. This final claim regarding Pareto improvement formally assumes that all beneficiaries have positive willingness to pay, which is natural in many of our contexts in which the policies expanded the choice sets of all beneficiaries.

These programs allow researchers to easily modify the set of input assumptions into each MVPF beyond the robustness we readily provide in the article and the Online Appendix. We provide an extended discussion of these in the Online Appendix. Because the cost of crime impacts are often difficult to quantify, they are not included in our baseline analyses when crime estimates are available, we we incorporate them in alternative specifications discussed in the Online Appendix for each policy.

Our confidence interval, however, includes both 1 and infinity. The edited volume from Weimer provides a discussion of cost-benefit analyses from different researchers in a range of different domains. Traditional cost-benefit approaches include fiscal externalities in the numerator see Greenberg, Deitch, and Hamilton In traditional cost-benefit analysis, increases in government tax revenue are included in the numerator of the expression.

More generally, we omit many potentially relevant categories of policies, such as macroeconomic stabilization, infrastructure investment, and environmental policies. For now, we do not place any assumption on these weights, and therefore they can result from any particular social welfare function. We also assume the weights do not change in response to the policy, but this is without loss of generality because we focus on small policy changes below.

We abstract from any behavioral biases in the utility function that might cause willingness to pay to be incongruent with choices that maximize well-being. Moreover, in practice, our approaches to inferring willingness to pay often require assumptions of rationality in individual utility that do not account for the potential presence of behavioral biases.

Traditional approaches would attempt to account for government spending by modifying the observed policy into a different policy that raised revenues via lump-sum taxation. In contrast, our approach hypothetically closes the budget constraint by comparing two MVPFs: one that involves an increase in spending and another that involves a reduction in spending or increase in revenue. Hence, welfare analysis can be done with two sets of causal effects one for the two policies under consideration as opposed to attempting to measure the compensated effect of a policy.

Including willingness to pay from parental altruism would only reinforce our central results. Similarly, we do not incorporate individual willingness to pay for redistribution to others. In practice an expenditure policy may have been combined with a separate tax policy to raise revenue at the time the policy is implemented.

In this case, the combined expenditure and tax policy would not deliver a Pareto improvement, as some current taxpayers would be made worse off. However, the infinite MVPF corresponds to a case where the government need not raise revenue to implement a policy that does not cost money in the long-run. The government could have borrowed against the future returns on the policy and generated a Pareto improvement.

This same logic would also apply if considering a large-scale expenditure on a policy that had previously been analyzed with a narrower RCT—one would have to make the additional assumption that the average treatment effect of this expanded policy is given by the effect identified in the RCT. If multiple papers analyze the same causal effect, we generally focus on the most recent published estimates unless otherwise noted.

We provide a detailed discussion of the alternative specifications in the Online Appendix. We exclude purely cross-sectional identification using controls for observables in our baseline sample. Within the set of experimental and quasi-experimental studies, we do not impose our own filter on the quality or validity of these empirical designs. In particular, we conservatively account for correlations across estimates in a given policy, and we develop a method to adjust for the uncertainty in the denominator with many thanks to conversations with Isaiah Andrews.

We provide the intuition for the approach and Monte Carlo simulations with appropriate coverage. In fact, the coverage is sometimes overly conservative, especially when costs approach 0. Zimmerman calculates costs and student contributions using the data on educational expenditures from the Delta Cost Project American Institutes for Research We adopt this approach for other college policies analyzed in our sample.

Online Appendix B explains the details of our approach. Zimmerman does not include any information on attendance of federally supported graduate schools among marginal FIU enrollees. If that information were available, it could be incorporated as an additional fiscal cost. We toggle these discount rates in our robustness discussion in Section IV. To be conservative, we exclude payroll taxes because individuals may benefit from a portion of these contributions. More detail on our calculations can be found in Online Appendix G.

The tax and transfer rate includes federal and state income taxes along with food stamps, but excludes housing vouchers and other welfare programs. We use the income-specific rate from the CBO estimates, and we apply this rate uniformly across years for simplicity. With more reliable historical information on marginal tax and transfer rates across the income distribution, one could perform the analysis separately by year.

We are not aware of any comprehensive historical source on the distribution of those rates. For this reason, we take the simpler approach of using a consistent tax and transfer rate and then assessing the robustness of all our results to alternative rate assumptions. We present robustness to alternative tax and transfer rate assumptions in Section IV.

Although this is a strong assumption, we show in the robustness analysis that our results are actually not very sensitive to the method we use to construct these forecasts. For example, we conduct a conservative forecast that assumes zero income growth over the life cycle. We refrain from incorporating general equilibrium effects in our willingness to pay due to a lack of evidence on this point.

If higher educational attainment produced positive spillovers on others, aggregate willingness to pay would rise. If the college earnings premium were driven by signaling effects, then we would expect other individuals to have a negative willingness to pay. However, if effects on income of admission to FIU is the result of higher levels of effort, that would require an adjustment for the disutility of labor and our baseline approach would overstate WTP; conversely, if individuals derive additional utility from attending college that is not captured in their earnings, the baseline approach would understate willingness to pay.

Our analysis also explores other policies that expanded Medicaid to children, such as the national expansion of Medicaid to those born after September 30, These policy changes correspond to separate MVPF constructions because they arise from different sources of policy variation. We assume no impact on children at older ages, but clearly such effects could alter the MVPF.

In Section V , we discuss the implications for a policy targeted to families with children aged 0—5; this leads to a larger MVPF. Our costs estimates here are constrained by the set of observed outcomes that we can reliably translate into effects on the government budget. For example, Hoynes, Schanzenbach, and Almond report that the introduction of food stamps was associated with a reduction in adult metabolic syndromes.

Although our earnings estimates likely capture the effect of those health changes on labor supply, we lack a reliable way to measure the impact of those health changes on health care utilization. Future work documenting long-run health impacts that reduce increase government spending on medical care could lead to a higher lower MVPF than we estimate here.

We find that the MVPF rises from 1. As discussed in Online Appendix D , the current set of results from the third year do not include sufficient information to form the MVPF in as precise a manner as we do here; but we note how imposing a reasonable additional assumption suggests that the third-year effects lead to a very similar MVPF also near 1. In the context of an RCT, our approach measures the welfare impact of randomly assigning additional people to the treatment as opposed to the control group.

As a result, one can use the reduced-form results to form our welfare analysis i. The denominator is the causal effect of this assignment on the budget and the numerator is the aggregate WTP by members of the control group to be in the treatment group.

As a result, whether our welfare analysis can be externally generalized to a different policy with different take-up of benefits would depend on how its treatment effects vary across the population. This calculation assumes no intensive-margin responses. If one observed the microdata from the RCT, one could allow for intensive-margin responses.

To the first order, the WTP is the mechanical change in the tax schedule i. In practice, this number is rarely reported, but future work conducting welfare analyses of RCTs can directly construct this measure. If the provision of work subsidies today leads to an increase in labor earnings and thus tax revenue after the earnings subsidies have ended, then the MVPF would be higher.

To ensure our conclusions are not biased by including policies for adults that do not have long-run follow-ups, in Section IV. C we conduct robustness of all our analysis to policies where long-run follow-ups have been measured. As discussed in Online Appendix C , we form this estimate by summing the observed increase in tax revenue for years 6—20 in administrative data from Schochet combined with an application of the CBO tax rate to the earnings effects for the first five years.

This is driven by a low tax rate on initial earnings declines and a comparatively higher tax rate on subsequently small earnings gains. A pure revealed-preference approach in this context could rely on the assumption that job training is accessible in the private market at its programmatic cost. One could then set willingness to pay equal to or perhaps below the up-front cost of program enrollment. In contrast, setting willingness to pay equal to after-tax earnings does not require the assumption that potential Job Corps enrollees have perfect information about the returns to job training at the time of initial enrollment.

However, it does require that after-tax income is sufficient to capture willingness to pay. This means we do not incorporate any welfare costs from optimization errors in consumption decisions that stem from program participation. Our analysis here focuses on the MVPF of the entire treatment group.

However, it is worth noting that Schochet finds larger effects for the subsample of age 20—24 participants, including a 2. Online Appendix F provides a derivation. For example, when analyzing the Movement to Opportunity MTO experiment, which provided housing vouchers and counseling to parents with children, the age shown is the average age of the children in the household.

This is because the policy induced higher earnings among the children, leading them to have a higher WTP for the policy than their parents. This working paper includes estimated impacts through age 55; our back-of-the-envelope calculations suggest that it is likely that forecasting these effects through 65 would lead the policy to pay for itself as well.

To harmonize these estimates with other programs, we do not include the benefits to the government from reduced crime. This is both because these costs are difficult to quantify and most papers do not estimate impacts on crime outcomes. If we include a forecast of reduced government spending on the criminal justice system and policing, our point estimates suggest that Perry Preschool paid for itself. However, the standard errors of these estimates also significantly increase. Including these costs for Abecedarian also increases its MVPF, but the policy does not appear to pay for itself.

It is important to note that we only analyze one paper on K—12 education spending because of limitations in existing evidence on long-term outcomes. While there is a large literature looking at the effect of school spending on test scores, we lack a reliable method to translate these effects into long-run impacts. Jackson, Persico, and Johnson demonstrate the potential for high returns to K—12 education, but future work is needed to robustly establish the presence of high returns to K—12 investment.

It is important to be clear that although our estimates suggest high returns to policies investing in older youth, the policies in our sample affect a range of subpopulations. As a result, further work is needed to assess how the rate of return on investment varies for a given child over the life cycle. We construct this average measure, as opposed to a precision-weighted average or other measure, because it corresponds to a feasible policy at the time of initial implementation. It is straightforward for the government to construct a policy that spends an equal amount on each of these programs.

Analogously, Appendix Figure II presents willingness to pay per dollar of programmatic spending. The one potential exception to this is the recent Year Up RCT, analyzed in Fein and Hamadyk , who document large increases in earnings in the two years after initial implementation. In addition, in estimates outside of our sampling frame, the nine-year follow-up results from the sectoral training program Project Quest suggest an MVPF of 1.

This suggests a high value to future work estimating the continued persistence of these more promising sectoral training programs. Our analysis also demonstrates the limitations of the traditional way that research papers report the impact of college expenditures. For this reason, there are cases where we find substantially different MVPFs for policies that had similar percentage point enrollment effects.

C we discuss how our results on college expenditures vary with the method of our MVPF calculation. Although we find persistently high MVPFs when long-run earnings outcomes are observed, we find lower MVPFs when we project earnings gains from attainment outcomes.

Those adult health insurance estimates include expenditures such as the subsidies in the Massachusetts health insurance exchange prior to the Affordable Care Act. Translating these estimates into an MVPF suggests values ranging from 0. For this reason, we calculate the MVPFs of job-training programs based on the number of years of earnings effects observed, rather than projecting the effects out to age In Online Appendix C we discuss the sensitivity of our results to that assumption.

The presence of high MVPFs for spending on children and low MVPFs for spending on adults does not necessarily indicate that families are failing to optimize their investment decisions. Even if families are fully informed of available investment decisions, a simple model of parental investment could produce these outcomes if parents are credit constrained.

A higher MVPF for investment in children could occur if low-income parents expect intergenerational regression to the mean such that their children will earn more than them. That would produce lower marginal utilities of income for those children, and therefore increase the return on spending. In addition, this logic also suggests that when parents are given cash transfers, they would rationally not spend all of it on their children despite high returns—this is because their marginal utility of their own consumption is also high.

As we discuss in Online Appendix F , these estimates appear to have considerable uncertainty not just from sampling uncertainty but also model uncertainty: using different taxable income estimates from existing literature studying these reforms can generate wide variation in the MVPFs of these tax reforms, preventing precise conclusions about their MVPFs.

Not all policies providing benefits to parents generate such large spillover effects onto children. In other cases, such as the provision of housing vouchers in Chicago, and the provision of housing vouchers to families on AFDC and the expansion of AFDC benefits, there is suggestive evidence that positive spillovers on children are small.

In those cases, researchers have documented that the policies have limited effects on outcomes such as test scores, college attendance, and birthweight. The college effects are restricted to a small subset of recipients, so it is unsurprising that the MVPFs remain small. We exclude these results from the baseline estimates because Bastian and Michelmore do not estimate the effect of a particular EITC expansion, but rather pool across many state and federal policy changes.

In Online Appendix F , we note the impact of incorporating their estimates. The fact that these impacts matter is consistent with our broader conclusions that potential spillovers onto children can generate high MVPFs for adult-targeted policies. Online Appendix J details an extensive set of robustness analyses. Related to this, the pattern of higher MVPFs for children could be driven by longer payoff periods for children relative to adults, as children have their entire lives to experience higher earnings.

However, the length of the payoff period is not what is driving our results—even restricting child benefits to accrue only up to age 45 or 55, we find similar high returns for child-targeted policies. Rather, the patterns are generally driven by a higher positive impact on per-year future earnings for policies targeting children. The one exception to this pattern is the low average MVPF among child policies implemented in the s. There are a range of forms that these political constraints might take.

For example, it could be that governments and politicians apply a much higher discount rate, requiring projects to pay off over short horizons. Alternatively, underinvestment might occur because these policies require spending by state and local governments, but much of the benefits accrue to the federal tax system.

Hence, local incentives may not be sufficient to make efficient investments. It may also be that the high MVPF policies are undersupported because low-income children have little political power. We leave a formal analysis of these potential mechanisms for future work. We thank Isaiah Andrews and Max Kasy for their invaluable guidance in implementing these procedures.

A publication bias of 35x is the degree of publication bias documented in Andrews and Kasy for small-sample experimental economics studies. It is worth noting, however, that there is considerable debate over the fiscal externalities associated with the EITC. On the one hand, several recent papers have argued that reductions in transfers have offset a substantial portion of the cost of historical EITC expansions Hoynes and Patel ; Bastian and Jones On the other hand, recent debates have argued that the effects are overstated in the current literature because the impact of the EITC expansions cannot be disentangled from the effects of contemporaneous welfare reforms Kleven These conflicting estimates suggest there is a high value to future work that reconciles these findings.

We develop this argument formally in Online Appendix L , where we relate this logic to the nonexistence of a social welfare function that can rationalize our results of high MVPFs for low-income children but low MVPFs for low-income adults. There is a large theoretical debate on this question, which largely centers around the applicability of the Atkinson-Stiglitz theorem Atkinson and Stiglitz ; Hylland and Zeckhauser In relation to the Atkinson-Stiglitz theorem, the violation of the weak separability assumption for these policies comes not from a short-term change in earnings but from the long-run indirect impact on children.

If the tag were manipulable, then individuals not intended as beneficiaries of the policy could distort their behavior to obtain the benefit. To the first order, they would not value the transfer by the envelope theorem, consequently lowering the MVPF of the policy. The focus here is on reducing uncertainty among the observed outcomes of each program. Uncertainty regarding unobserved causal effects remains beyond the scope of this exercise.

For simplicity, we assume programmatic costs are known and equal to their point estimates. Welfare reform experiments were expected to place no additional costs on the federal government and so it is natural that states bundled increases in some types of financial support with potential decreases in others. Previous work Greenberg, Deitch, and Hamilton has conducted a cost-benefit analysis of these reforms by assuming willingness to pay is given by after-tax earnings.

However, if the term limit is what causes individuals to choose to move off of welfare and into the labor market thus increasing earnings , the envelope theorem would suggest the WTP is negative, even if after-tax earnings increase. Welfare reform RCTs have been criticized for not experimentally varying each of the components of welfare reform see Grogger and Karoly The MVPF framework suggests bundling carrots and sticks into a single treatment is particularly problematic for conducting welfare analysis, because it is difficult to know even whether willingness to pay is positive or negative.

In fact, we find policies that follow this pattern in each subcategory of welfare reform programs. These subcategories include job search assistance. Abraham Katharine G. Google Scholar. Akerlof George A. Almond Douglas , Hoynes Hilary W. Atkinson Anthony B. Auerbach and M. Feldstein, eds. Amsterdam: Elsevier , , 61 — Google Preview. Auerbach Alan J. Amsterdam: Elsevier , , — Autor David H. Barnett W. Steven , Masse Leonard N.

Bartik Timothy J. Bastian Jacob , Jones Maggie R. Chicago: University of Chicago Press , , — Bettinger Eric P. Black Sandra E. Blank Rebecca M. Bloom Dan , Kemple James J. Bloom Howard S. Boudreaux Michel H. Brown David , Kowalski Amanda E. Brown David W. Bruce Donald J. Bulman George B. Camerer Colin F. Campbell Frances A. Carruthers Celeste K. Castleman Benjamin L. Chetty Raj , Friedman John N. Cohodes Sarah R. Sign In.

Advanced Search. Search Menu. Skip Nav Destination Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. MVPF Framework. Main Results: Targeting Kids versus Adults. Lessons for Future Work. Article Navigation.

Editor's Choice. Harvard University. Oxford Academic. Ben Sprung-Keyser. Select Format Select format. Permissions Icon Permissions. Abstract We conduct a comparative welfare analysis of historical policy changes over the past half-century in the United States, focusing on policies in social insurance, education and job training, taxes and cash transfers, and in-kind transfers. Consider a government seeking to measure the welfare impact of a government policy change under consideration.

We parameterize the up-front initial spending on policy j by dp j which can either be an increase or decrease. The MVPF uses many of the same underlying estimates used to create benefit-cost ratios, but combines them in a different way. Age of. Open in new tab. Open in new tab Download slide. The cost of the policy is the observed causal effect of the policy on the government budget. WTP CI. Cost CI. To reach more precise conclusions at a broader level, we pool across policies using category averages.

Net Government Costs per Dollar of Programmatic Spending This figure presents estimates of costs normalized by initial programmatic for each category-average group of policies in our baseline sample. Children estimates. Adult estimates. Value of Information This figure presents the value of information, v info , discussed in Section VI.

For simplicity, we assume the policy is financed with a tax change that targets the same beneficiaries and has an MVPF of 1. We begin by considering a case where spending v j allows the policy maker to perfectly learn WTP j and G j before deciding whether to invest in the policy. Now it can decide to pursue the policy if and only if the true WTP exceeds the true costs. To illustrate this decision, we consider the case of the introduction of food stamps discussed in Section III.

Here, we imagine that a policy maker is deciding whether to introduce food stamps based on the existing evidence. The policy maker will expect these estimates to be drawn from the PSID sampling distribution but contain the standard errors found in the census data estimates. Comparison to CBA This figure presents estimates of benefit-cost ratios for all policies evaluated in the article and shows their relationship to the MVPF.

Appendix Figure i. Appendix Figure ii. Willingness to Pay per Dollar of Programmatic Spending This figure presents estimates of WTP normalized by initial programmatic spending for each category-average group of policies in our baseline sample. Appendix Figure iii. Robustness to Child Effects This figure assesses the impact of observing child effects on our estimates as a function of the average age of the economic beneficiaries of the policy. Appendix Figure iv.

Appendix Figure v. Appendix Figure vi. Specification Robustness This figure presents the category-average MVPFs from Figure III using a range of different alternative specifications that are more conservative than our baseline specifications. Appendix Figure vii. Appendix Figure viii. Appendix Figure ix. Appendix Figure x. Google Scholar Crossref. Search ADS. American Institutes for Research. Barnett and Masse Helburn Masse and Barnett Masse Johnson and Jackson Ludwig and Miller Kline and Walters Hyman Bulman and Hoxby Turner Long Seftor and Turner Hoxby and Bulman LaLumia Marx and Turner Acton Denning Abraham and Clark Zimmerman Castleman and Long Department of Education.

Office of Postsecondary. Education Cohodes and Goodman , Goodman Bettinger Carruthers and Welch Denning et al. Dynarski Deming and Walters Bruce and Carruthers Schochet Hollister, Kemper, and Maynard Couch Schaberg Fein and Hamadyk Gelber, Moore, and Strand Maestas, Mullen, and Strand French and Song Hendren c. Finkelstein, Hendren, and. Shepard Census Bureau Finkelstein and McKnight Luttmer Cabral and Mahoney Lo Sasso and Seamster Wherry and Meyer Currie and Gruber Miller and Wherry Boudreaux, Golberstein, and McAlpine Brown, Kowalski, and Lurie Goodman-Bacon Deshpande Social Security Administration Gruber Hendren b.

Diff Across States in Kroft. Kroft and Notowidigdo Schmieder and Von Wachter Solon

BET ON BLAC OR RED IN ROULETTE

This is, of course, a stylized exercise. We are imagining a policy maker that sees the ex post evaluation of a policy prior to making her decision—something that is clearly not feasible. The goal here is merely to illustrate potential value of expanding access to administrative data sets that can generate more precise estimates of long-run policy effects.

While we focus on computing the MVPF for each policy, the most common form of welfare analysis in previous literature is benefit-cost analysis, as in equation 4. With that in mind, we compare our results to the benefit-cost ratios for the same policies. Our general conclusion about the high returns to investment in children would remain true even if one used a benefit-cost ratio instead of the MVPF.

The average benefit-cost ratio is 4. In contrast, we find smaller benefit-cost ratios for adult policies—often less than 1. This figure presents estimates of benefit-cost ratios for all policies evaluated in the article and shows their relationship to the MVPF. The method for calculating these benefit-cost ratios is outlined in Section II. Panel A plots the benefit-cost ratio of each policy as a function of the age of the beneficiaries, along with category average estimates and their confidence intervals.

Panel B plots the benefit-cost ratio of each policy as a function of the MVPF estimate for the policy. Panels A and B present a decomposition of the elements that make up our income projection process for the examples in Section III. This number is constructed by assuming that the mean wage level at each age will rise and has previously risen by 0. See Online Appendix I for further details of this methodology. This figure presents estimates of WTP normalized by initial programmatic spending for each category-average group of policies in our baseline sample.

The normalized willingness to pay of individual policies are shown in smaller dots, color-coded to align with their respective categories. This figure assesses the impact of observing child effects on our estimates as a function of the average age of the economic beneficiaries of the policy. Panel A restricts our sample to the subset of policies for which we observe estimates of the impact of the policy on children.

In addition, Panel B shows projected MVPFs for additional policies that do not observe earnings impacts but do observe another intermediate outcome such as birthweight AFDC , college attendance housing vouchers to AFDC recipients , and test scores housing vouchers in Chicago. Panel B differs slightly from our baseline specification because we restrict to the subset of policies for which we are able to vary the discount rate e.

We omit confidence intervals for ease of viewing, but caution the reader that the estimate for the college adult category has a CI that includes 0 and infinity. Panel A replicates our baseline specification using the CBO estimates of the tax rates. This figure presents the category-average MVPFs from Figure III using a range of different alternative specifications that are more conservative than our baseline specifications.

Panel B replaces our baseline income projection procedure with a procedure that assumes zero income growth over the lifecycle. We use our restricted sample of policies for this specification. See Online Appendix I for further details.

Panel B restricts the sample to only policies for which earnings outcomes are estimated for at least five years of follow-up after the policy. For this panel we show group averages even for groups with a single policy. Panel C restricts the sample to policies whose identification strategy is a randomized control trial, lottery, or regression discontinuity.

Panel D restricts to policies whose primary analyses have been published in a peer-reviewed journal. We do not report confidence intervals for these estimates to our knowledge there is no well-accepted method of constructing such intervals ; but we refer readers to Figure IV , Panel B to note that some of these category averages are imprecise. This figure presents MVPFs for all policies evaluated in the article based on the year in which the policy was implemented.

Policies are divided into categories based on their decade of implementation and the average age of their economic beneficiaries. For policies implemented in each decade there are two categories— policies with beneficiaries over age 23 and policies with beneficiaries aged 23 or younger. This is the same approach used to create MVPF estimates for policy domains in previous figures. We report MVPF estimates using three potential measures of WTP: i cost, the mechanical cost of the program incurred by the government, excluding any fiscal externalities from behavior change.

Estimates from this specification are denoted by circles. Estimates from this specification are denoted by Xs. Estimates from this specification are denoted by triangles. In general, we find a fairly monotonic relationship—policies with high benefit-cost ratios also have high MVPFs. There are, however, some notable distinctions. Similarly, the top tax rate reduction has an infinite MVPF but a benefit-cost ratio of 1.

By the standards of benefit-cost ratios, these policies may not appear all that desirable, even though the MVPF point estimates imply that they pay for themselves and provide a Pareto improvement. The difference between the MVPF and benefit-cost ratio in these cases reflects the fact that the benefit-cost ratio places all causal effects of the program in the numerator while the MVPF incorporates effects based on their incidence.

In particular, the numerator of the MVPF captures the effects on beneficiaries while the denominator captures all effects on the government budget. In measuring the welfare effects of the Medicaid expansion and the tax cut, MVPF places all fiscal externalities in the denominator.

The results show us that these policies have substantial benefits and limited or no net government cost. In the benefit-cost ratio framework these reforms would have been interpreted as high-cost policies with substantial benefits. The second crucial distinction between the MVPF and benefit-cost ratio is how the two approaches conceptually close the budget constraint.

In many cases, benefit-cost analysis includes no discussion of closing the budget constraint. In cases where the concept is addressed, it is customary to close the budget constraint in the same manner regardless of the policy context.

To see how this matters, consider the tax reform that simultaneously raised top marginal income tax rates and expanded the EITC. One could, in principle, use a benefit-cost ratio to evaluate whether the EITC expansion was desirable. The costs exceed the benefits and so, if the government were applying a strict benefit-cost test, we would not expect this policy to be implemented. That said, the goal of the EITC expansion was to provide redistributive benefits to low-income workers.

Its MVPF is 1. As noted, the MVPF point estimate for the top tax rate change is 1. Throughout, we aimed to include all possible MVPFs in the categories we considered. We included any policy where we thought we could provide reasonable measures of both costs and WTP. One set of notable omissions are the state-level welfare reforms made by states that sought to increase family self-sufficiency. Throughout the s and early s, states experimented with a range of reforms to cash welfare programs that imposed term limits, provided job training and other educational services, and provided job search and placement assistance.

The omission of these reforms is not because they were not analyzed. Many states rigorously evaluated the effect of these reforms. These RCTs measured and provided a clear estimate of the net cost of each reform. However, the design of the policies enacted in each state makes it difficult to understand their welfare effects. Generally, programs contained both a carrot and a stick. It may be that government revenue gains were the result of expanded job opportunities due to program participation.

In that case, willingness to pay would be positive. By contrast, it may be that the government revenue gains were the result of stricter attendance requirements that drove individuals off welfare. In that case, willingness to pay would be negative. This highlights the value of isolating the carrot and the stick into separate RCTs. In Appendix Figure X , we conduct a range of bounding exercises that attempt to construct lower and upper bounds on WTP for these welfare reform programs.

Unfortunately, the bounds are very wide. In this article, we examine the MVPF of different historical policies over the past half-century in the United States. We find a clear and persistent pattern that direct investments in children have yielded the largest MVPFs. We also demonstrate that in a meaningful number of cases, these policies pay for themselves. From a taxpayer perspective, these expenditures on children are investments, rather than just transfers.

We find that opportunities for high-return investments in children have persisted across policy categories for many decades. This is, however, no guarantee that all future investment in these categories will produce high MVPFs. Indeed, we find that MVPFs vary substantially within policy categories.

Low-return policies exist even in high-return categories. This highlights the value of further understanding the mechanisms behind the high MVPFs of successful historical investments. Even in cases where there is existing research, much remains unknown about the welfare consequences of government policy.

To that aim, we quantify the value of future work that uses new data to reduce estimate uncertainty. We show that in many cases, an evidence-based policy maker seeking to maximize social welfare should be willing to make substantial budgetary expenditures to learn more about policy effectiveness.

In particular, our results highlight the value of expanded use of administrative data for policy analysis. The policies included in this article are just a small subset of those that could be analyzed using the MVPF. We do not discuss the MVPF of crime policies, environmental policies, macroeconomic stabilization policies, or infrastructure policies, among many others. With careful tracking of willingness to pay and net costs, the MVPF can be used in any of these contexts and can guide cost-benefit analyses.

We leave that analysis for future work. We first and foremost thank the several hundred researchers whose empirical results form the foundation of our estimates. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author s and do not necessarily reflect the views of the National Science Foundation. See Mayshar , Slemrod and Yitzhaki , , and Kleven and Kreiner for original definitions, and Hendren for a comparison of the MVPF to alternative measures of welfare.

In several cases where authors constructed their own MVPFs, we incorporate those estimates directly. Where applicable, we adjust these estimates to harmonize assumptions e. In cases where previous literature has conducted comprehensive cost-benefit analyses of a policy, we draw on the components of those analyses to reformulate them into their implied MVPF. The intuition here comes from the envelope theorem.

Additional costs due to behavioral responses are not valued dollar for dollar. To align with terminology in existing literature, we use various terms interchangeably to refer to the same phenomenon. Any policy with a positive willingness to pay and negative net costs we define to have an infinite MVPF. This is because the expenditure is valued by beneficiaries and has no net cost on the government. This final claim regarding Pareto improvement formally assumes that all beneficiaries have positive willingness to pay, which is natural in many of our contexts in which the policies expanded the choice sets of all beneficiaries.

These programs allow researchers to easily modify the set of input assumptions into each MVPF beyond the robustness we readily provide in the article and the Online Appendix. We provide an extended discussion of these in the Online Appendix. Because the cost of crime impacts are often difficult to quantify, they are not included in our baseline analyses when crime estimates are available, we we incorporate them in alternative specifications discussed in the Online Appendix for each policy.

Our confidence interval, however, includes both 1 and infinity. The edited volume from Weimer provides a discussion of cost-benefit analyses from different researchers in a range of different domains. Traditional cost-benefit approaches include fiscal externalities in the numerator see Greenberg, Deitch, and Hamilton In traditional cost-benefit analysis, increases in government tax revenue are included in the numerator of the expression. More generally, we omit many potentially relevant categories of policies, such as macroeconomic stabilization, infrastructure investment, and environmental policies.

For now, we do not place any assumption on these weights, and therefore they can result from any particular social welfare function. We also assume the weights do not change in response to the policy, but this is without loss of generality because we focus on small policy changes below.

We abstract from any behavioral biases in the utility function that might cause willingness to pay to be incongruent with choices that maximize well-being. Moreover, in practice, our approaches to inferring willingness to pay often require assumptions of rationality in individual utility that do not account for the potential presence of behavioral biases. Traditional approaches would attempt to account for government spending by modifying the observed policy into a different policy that raised revenues via lump-sum taxation.

In contrast, our approach hypothetically closes the budget constraint by comparing two MVPFs: one that involves an increase in spending and another that involves a reduction in spending or increase in revenue. Hence, welfare analysis can be done with two sets of causal effects one for the two policies under consideration as opposed to attempting to measure the compensated effect of a policy.

Including willingness to pay from parental altruism would only reinforce our central results. Similarly, we do not incorporate individual willingness to pay for redistribution to others. In practice an expenditure policy may have been combined with a separate tax policy to raise revenue at the time the policy is implemented. In this case, the combined expenditure and tax policy would not deliver a Pareto improvement, as some current taxpayers would be made worse off.

However, the infinite MVPF corresponds to a case where the government need not raise revenue to implement a policy that does not cost money in the long-run. The government could have borrowed against the future returns on the policy and generated a Pareto improvement. This same logic would also apply if considering a large-scale expenditure on a policy that had previously been analyzed with a narrower RCT—one would have to make the additional assumption that the average treatment effect of this expanded policy is given by the effect identified in the RCT.

If multiple papers analyze the same causal effect, we generally focus on the most recent published estimates unless otherwise noted. We provide a detailed discussion of the alternative specifications in the Online Appendix. We exclude purely cross-sectional identification using controls for observables in our baseline sample.

Within the set of experimental and quasi-experimental studies, we do not impose our own filter on the quality or validity of these empirical designs. In particular, we conservatively account for correlations across estimates in a given policy, and we develop a method to adjust for the uncertainty in the denominator with many thanks to conversations with Isaiah Andrews.

We provide the intuition for the approach and Monte Carlo simulations with appropriate coverage. In fact, the coverage is sometimes overly conservative, especially when costs approach 0. Zimmerman calculates costs and student contributions using the data on educational expenditures from the Delta Cost Project American Institutes for Research We adopt this approach for other college policies analyzed in our sample.

Online Appendix B explains the details of our approach. Zimmerman does not include any information on attendance of federally supported graduate schools among marginal FIU enrollees. If that information were available, it could be incorporated as an additional fiscal cost. We toggle these discount rates in our robustness discussion in Section IV. To be conservative, we exclude payroll taxes because individuals may benefit from a portion of these contributions.

More detail on our calculations can be found in Online Appendix G. The tax and transfer rate includes federal and state income taxes along with food stamps, but excludes housing vouchers and other welfare programs. We use the income-specific rate from the CBO estimates, and we apply this rate uniformly across years for simplicity. With more reliable historical information on marginal tax and transfer rates across the income distribution, one could perform the analysis separately by year.

We are not aware of any comprehensive historical source on the distribution of those rates. For this reason, we take the simpler approach of using a consistent tax and transfer rate and then assessing the robustness of all our results to alternative rate assumptions. We present robustness to alternative tax and transfer rate assumptions in Section IV.

Although this is a strong assumption, we show in the robustness analysis that our results are actually not very sensitive to the method we use to construct these forecasts. For example, we conduct a conservative forecast that assumes zero income growth over the life cycle. We refrain from incorporating general equilibrium effects in our willingness to pay due to a lack of evidence on this point. If higher educational attainment produced positive spillovers on others, aggregate willingness to pay would rise.

If the college earnings premium were driven by signaling effects, then we would expect other individuals to have a negative willingness to pay. However, if effects on income of admission to FIU is the result of higher levels of effort, that would require an adjustment for the disutility of labor and our baseline approach would overstate WTP; conversely, if individuals derive additional utility from attending college that is not captured in their earnings, the baseline approach would understate willingness to pay.

Our analysis also explores other policies that expanded Medicaid to children, such as the national expansion of Medicaid to those born after September 30, These policy changes correspond to separate MVPF constructions because they arise from different sources of policy variation. We assume no impact on children at older ages, but clearly such effects could alter the MVPF. In Section V , we discuss the implications for a policy targeted to families with children aged 0—5; this leads to a larger MVPF.

Our costs estimates here are constrained by the set of observed outcomes that we can reliably translate into effects on the government budget. For example, Hoynes, Schanzenbach, and Almond report that the introduction of food stamps was associated with a reduction in adult metabolic syndromes. Although our earnings estimates likely capture the effect of those health changes on labor supply, we lack a reliable way to measure the impact of those health changes on health care utilization.

Future work documenting long-run health impacts that reduce increase government spending on medical care could lead to a higher lower MVPF than we estimate here. We find that the MVPF rises from 1. As discussed in Online Appendix D , the current set of results from the third year do not include sufficient information to form the MVPF in as precise a manner as we do here; but we note how imposing a reasonable additional assumption suggests that the third-year effects lead to a very similar MVPF also near 1.

In the context of an RCT, our approach measures the welfare impact of randomly assigning additional people to the treatment as opposed to the control group. As a result, one can use the reduced-form results to form our welfare analysis i. The denominator is the causal effect of this assignment on the budget and the numerator is the aggregate WTP by members of the control group to be in the treatment group.

As a result, whether our welfare analysis can be externally generalized to a different policy with different take-up of benefits would depend on how its treatment effects vary across the population. This calculation assumes no intensive-margin responses. If one observed the microdata from the RCT, one could allow for intensive-margin responses. To the first order, the WTP is the mechanical change in the tax schedule i.

In practice, this number is rarely reported, but future work conducting welfare analyses of RCTs can directly construct this measure. If the provision of work subsidies today leads to an increase in labor earnings and thus tax revenue after the earnings subsidies have ended, then the MVPF would be higher.

To ensure our conclusions are not biased by including policies for adults that do not have long-run follow-ups, in Section IV. C we conduct robustness of all our analysis to policies where long-run follow-ups have been measured. As discussed in Online Appendix C , we form this estimate by summing the observed increase in tax revenue for years 6—20 in administrative data from Schochet combined with an application of the CBO tax rate to the earnings effects for the first five years.

This is driven by a low tax rate on initial earnings declines and a comparatively higher tax rate on subsequently small earnings gains. A pure revealed-preference approach in this context could rely on the assumption that job training is accessible in the private market at its programmatic cost. One could then set willingness to pay equal to or perhaps below the up-front cost of program enrollment.

In contrast, setting willingness to pay equal to after-tax earnings does not require the assumption that potential Job Corps enrollees have perfect information about the returns to job training at the time of initial enrollment. However, it does require that after-tax income is sufficient to capture willingness to pay. This means we do not incorporate any welfare costs from optimization errors in consumption decisions that stem from program participation. Our analysis here focuses on the MVPF of the entire treatment group.

However, it is worth noting that Schochet finds larger effects for the subsample of age 20—24 participants, including a 2. Online Appendix F provides a derivation. For example, when analyzing the Movement to Opportunity MTO experiment, which provided housing vouchers and counseling to parents with children, the age shown is the average age of the children in the household. This is because the policy induced higher earnings among the children, leading them to have a higher WTP for the policy than their parents.

This working paper includes estimated impacts through age 55; our back-of-the-envelope calculations suggest that it is likely that forecasting these effects through 65 would lead the policy to pay for itself as well. To harmonize these estimates with other programs, we do not include the benefits to the government from reduced crime.

This is both because these costs are difficult to quantify and most papers do not estimate impacts on crime outcomes. If we include a forecast of reduced government spending on the criminal justice system and policing, our point estimates suggest that Perry Preschool paid for itself. However, the standard errors of these estimates also significantly increase. Including these costs for Abecedarian also increases its MVPF, but the policy does not appear to pay for itself.

It is important to note that we only analyze one paper on K—12 education spending because of limitations in existing evidence on long-term outcomes. While there is a large literature looking at the effect of school spending on test scores, we lack a reliable method to translate these effects into long-run impacts.

Jackson, Persico, and Johnson demonstrate the potential for high returns to K—12 education, but future work is needed to robustly establish the presence of high returns to K—12 investment. It is important to be clear that although our estimates suggest high returns to policies investing in older youth, the policies in our sample affect a range of subpopulations.

As a result, further work is needed to assess how the rate of return on investment varies for a given child over the life cycle. We construct this average measure, as opposed to a precision-weighted average or other measure, because it corresponds to a feasible policy at the time of initial implementation. It is straightforward for the government to construct a policy that spends an equal amount on each of these programs.

Analogously, Appendix Figure II presents willingness to pay per dollar of programmatic spending. The one potential exception to this is the recent Year Up RCT, analyzed in Fein and Hamadyk , who document large increases in earnings in the two years after initial implementation. In addition, in estimates outside of our sampling frame, the nine-year follow-up results from the sectoral training program Project Quest suggest an MVPF of 1. This suggests a high value to future work estimating the continued persistence of these more promising sectoral training programs.

Our analysis also demonstrates the limitations of the traditional way that research papers report the impact of college expenditures. For this reason, there are cases where we find substantially different MVPFs for policies that had similar percentage point enrollment effects. C we discuss how our results on college expenditures vary with the method of our MVPF calculation. Although we find persistently high MVPFs when long-run earnings outcomes are observed, we find lower MVPFs when we project earnings gains from attainment outcomes.

Those adult health insurance estimates include expenditures such as the subsidies in the Massachusetts health insurance exchange prior to the Affordable Care Act. Translating these estimates into an MVPF suggests values ranging from 0. For this reason, we calculate the MVPFs of job-training programs based on the number of years of earnings effects observed, rather than projecting the effects out to age In Online Appendix C we discuss the sensitivity of our results to that assumption.

The presence of high MVPFs for spending on children and low MVPFs for spending on adults does not necessarily indicate that families are failing to optimize their investment decisions. Even if families are fully informed of available investment decisions, a simple model of parental investment could produce these outcomes if parents are credit constrained.

A higher MVPF for investment in children could occur if low-income parents expect intergenerational regression to the mean such that their children will earn more than them. That would produce lower marginal utilities of income for those children, and therefore increase the return on spending.

In addition, this logic also suggests that when parents are given cash transfers, they would rationally not spend all of it on their children despite high returns—this is because their marginal utility of their own consumption is also high. As we discuss in Online Appendix F , these estimates appear to have considerable uncertainty not just from sampling uncertainty but also model uncertainty: using different taxable income estimates from existing literature studying these reforms can generate wide variation in the MVPFs of these tax reforms, preventing precise conclusions about their MVPFs.

Not all policies providing benefits to parents generate such large spillover effects onto children. In other cases, such as the provision of housing vouchers in Chicago, and the provision of housing vouchers to families on AFDC and the expansion of AFDC benefits, there is suggestive evidence that positive spillovers on children are small. In those cases, researchers have documented that the policies have limited effects on outcomes such as test scores, college attendance, and birthweight.

The college effects are restricted to a small subset of recipients, so it is unsurprising that the MVPFs remain small. We exclude these results from the baseline estimates because Bastian and Michelmore do not estimate the effect of a particular EITC expansion, but rather pool across many state and federal policy changes.

In Online Appendix F , we note the impact of incorporating their estimates. The fact that these impacts matter is consistent with our broader conclusions that potential spillovers onto children can generate high MVPFs for adult-targeted policies. Online Appendix J details an extensive set of robustness analyses.

Related to this, the pattern of higher MVPFs for children could be driven by longer payoff periods for children relative to adults, as children have their entire lives to experience higher earnings. However, the length of the payoff period is not what is driving our results—even restricting child benefits to accrue only up to age 45 or 55, we find similar high returns for child-targeted policies.

Rather, the patterns are generally driven by a higher positive impact on per-year future earnings for policies targeting children. The one exception to this pattern is the low average MVPF among child policies implemented in the s. There are a range of forms that these political constraints might take. For example, it could be that governments and politicians apply a much higher discount rate, requiring projects to pay off over short horizons.

Alternatively, underinvestment might occur because these policies require spending by state and local governments, but much of the benefits accrue to the federal tax system. Hence, local incentives may not be sufficient to make efficient investments. It may also be that the high MVPF policies are undersupported because low-income children have little political power. We leave a formal analysis of these potential mechanisms for future work. We thank Isaiah Andrews and Max Kasy for their invaluable guidance in implementing these procedures.

A publication bias of 35x is the degree of publication bias documented in Andrews and Kasy for small-sample experimental economics studies. It is worth noting, however, that there is considerable debate over the fiscal externalities associated with the EITC. On the one hand, several recent papers have argued that reductions in transfers have offset a substantial portion of the cost of historical EITC expansions Hoynes and Patel ; Bastian and Jones On the other hand, recent debates have argued that the effects are overstated in the current literature because the impact of the EITC expansions cannot be disentangled from the effects of contemporaneous welfare reforms Kleven These conflicting estimates suggest there is a high value to future work that reconciles these findings.

We develop this argument formally in Online Appendix L , where we relate this logic to the nonexistence of a social welfare function that can rationalize our results of high MVPFs for low-income children but low MVPFs for low-income adults.

There is a large theoretical debate on this question, which largely centers around the applicability of the Atkinson-Stiglitz theorem Atkinson and Stiglitz ; Hylland and Zeckhauser In relation to the Atkinson-Stiglitz theorem, the violation of the weak separability assumption for these policies comes not from a short-term change in earnings but from the long-run indirect impact on children.

If the tag were manipulable, then individuals not intended as beneficiaries of the policy could distort their behavior to obtain the benefit. To the first order, they would not value the transfer by the envelope theorem, consequently lowering the MVPF of the policy. The focus here is on reducing uncertainty among the observed outcomes of each program.

Uncertainty regarding unobserved causal effects remains beyond the scope of this exercise. For simplicity, we assume programmatic costs are known and equal to their point estimates. Welfare reform experiments were expected to place no additional costs on the federal government and so it is natural that states bundled increases in some types of financial support with potential decreases in others.

Previous work Greenberg, Deitch, and Hamilton has conducted a cost-benefit analysis of these reforms by assuming willingness to pay is given by after-tax earnings. However, if the term limit is what causes individuals to choose to move off of welfare and into the labor market thus increasing earnings , the envelope theorem would suggest the WTP is negative, even if after-tax earnings increase. Welfare reform RCTs have been criticized for not experimentally varying each of the components of welfare reform see Grogger and Karoly The MVPF framework suggests bundling carrots and sticks into a single treatment is particularly problematic for conducting welfare analysis, because it is difficult to know even whether willingness to pay is positive or negative.

In fact, we find policies that follow this pattern in each subcategory of welfare reform programs. These subcategories include job search assistance. Abraham Katharine G. Google Scholar. Akerlof George A. Almond Douglas , Hoynes Hilary W. Atkinson Anthony B.

Auerbach and M. Feldstein, eds. Amsterdam: Elsevier , , 61 — Google Preview. Auerbach Alan J. Amsterdam: Elsevier , , — Autor David H. Barnett W. Steven , Masse Leonard N. Bartik Timothy J. Bastian Jacob , Jones Maggie R. Chicago: University of Chicago Press , , — Bettinger Eric P.

Black Sandra E. Blank Rebecca M. Bloom Dan , Kemple James J. Bloom Howard S. Boudreaux Michel H. Brown David , Kowalski Amanda E. Brown David W. Bruce Donald J. Bulman George B. Camerer Colin F. Campbell Frances A. Carruthers Celeste K. Castleman Benjamin L. Chetty Raj , Friedman John N.

Cohodes Sarah R. Sign In. Advanced Search. Search Menu. Skip Nav Destination Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. MVPF Framework. Main Results: Targeting Kids versus Adults. Lessons for Future Work.

Article Navigation. Editor's Choice. Harvard University. Oxford Academic. Ben Sprung-Keyser. Select Format Select format. Permissions Icon Permissions. Abstract We conduct a comparative welfare analysis of historical policy changes over the past half-century in the United States, focusing on policies in social insurance, education and job training, taxes and cash transfers, and in-kind transfers.

Consider a government seeking to measure the welfare impact of a government policy change under consideration. We parameterize the up-front initial spending on policy j by dp j which can either be an increase or decrease. The MVPF uses many of the same underlying estimates used to create benefit-cost ratios, but combines them in a different way. Age of. Open in new tab. Open in new tab Download slide. The cost of the policy is the observed causal effect of the policy on the government budget.

WTP CI. Cost CI. To reach more precise conclusions at a broader level, we pool across policies using category averages. Net Government Costs per Dollar of Programmatic Spending This figure presents estimates of costs normalized by initial programmatic for each category-average group of policies in our baseline sample. Children estimates. Adult estimates. Value of Information This figure presents the value of information, v info , discussed in Section VI.

For simplicity, we assume the policy is financed with a tax change that targets the same beneficiaries and has an MVPF of 1. We begin by considering a case where spending v j allows the policy maker to perfectly learn WTP j and G j before deciding whether to invest in the policy.

Now it can decide to pursue the policy if and only if the true WTP exceeds the true costs. To illustrate this decision, we consider the case of the introduction of food stamps discussed in Section III. Here, we imagine that a policy maker is deciding whether to introduce food stamps based on the existing evidence.

The policy maker will expect these estimates to be drawn from the PSID sampling distribution but contain the standard errors found in the census data estimates. Comparison to CBA This figure presents estimates of benefit-cost ratios for all policies evaluated in the article and shows their relationship to the MVPF.

Appendix Figure i. Appendix Figure ii. Willingness to Pay per Dollar of Programmatic Spending This figure presents estimates of WTP normalized by initial programmatic spending for each category-average group of policies in our baseline sample. Appendix Figure iii. Robustness to Child Effects This figure assesses the impact of observing child effects on our estimates as a function of the average age of the economic beneficiaries of the policy.

Appendix Figure iv. Appendix Figure v. Appendix Figure vi. Specification Robustness This figure presents the category-average MVPFs from Figure III using a range of different alternative specifications that are more conservative than our baseline specifications. Appendix Figure vii.

Appendix Figure viii. Appendix Figure ix. Appendix Figure x. Google Scholar Crossref. Search ADS. American Institutes for Research. Barnett and Masse Helburn Masse and Barnett Masse Johnson and Jackson Ludwig and Miller Kline and Walters Hyman Bulman and Hoxby Turner Long Seftor and Turner Hoxby and Bulman LaLumia Marx and Turner Acton Denning Abraham and Clark Zimmerman Castleman and Long Department of Education.

Office of Postsecondary. Education Cohodes and Goodman , Goodman Bettinger Carruthers and Welch Denning et al. Dynarski Deming and Walters Bruce and Carruthers Schochet Hollister, Kemper, and Maynard Couch Schaberg Fein and Hamadyk Gelber, Moore, and Strand Maestas, Mullen, and Strand French and Song Hendren c. Finkelstein, Hendren, and.

Shepard Census Bureau Finkelstein and McKnight Luttmer Cabral and Mahoney Lo Sasso and Seamster Wherry and Meyer Currie and Gruber Miller and Wherry Boudreaux, Golberstein, and McAlpine Brown, Kowalski, and Lurie Goodman-Bacon Deshpande Social Security Administration Gruber Hendren b.

Diff Across States in Kroft. Kroft and Notowidigdo Schmieder and Von Wachter Solon Meyer and Mok Landais Ganong and Noel Katz and Meyer Johnston and Mas Jacob and Ludwig Wood, Turnham, and Mills Jacob, Ludwig, and Kapustin Bloom, Riccio, and Verma Riccio Chetty, Hendren, and Katz Black, Devereux, and Salvanes Hoynes, Page, and Stevens Whitmore Finkelstein and Notowidigdo Hoynes, Schanzenbach, and Almond Almond, Hoynes, and Schanzenbach Hoynes and Schanzenbach East Ackerman, Holtzblatt, and Masken Tax Policy Center Blank and Ruggles Hotz and Scholz Meyer and Rosenbaum I do not want to be … a monster.

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Greenberg and Martha Doemland and Julie A. The effect of routine vaccination on invasive pneumococcal infections in Canadian children, Immunization Monitoring Program, Active — Vaccine Julie A. Scheifele and James D. Kellner and Scott A. Why collect individual-level vaccination data?

Rotavirus vaccination: results of a longitudinal study on determinants of Canadian parents' decision Canadian Journal of Infectious Diseases and Medical Microbiology Canadian family physicians' and paediatricians' opinions and intentions regarding hepatitis A and B infection and their prevention by vaccination Canadian Journal of Infectious Diseases and Medical Microbiology Surveillance for adverse events following adjuvanted pandemic influenza vaccine in Canadian children Canadian Journal of Infectious Diseases and Medical Microbiology Priorities for new vaccination programs implementation: paediatricians' and family physicians' opinions Canadian Journal of Infectious Diseases and Medical Microbiology Rotavirus serotypes results from an impact emergency department study Canadian Journal of Infectious Diseases and Medical Microbiology Canadian family physicians' and paediatricians' opinions toward A H1N1 pandemic vaccine before and after vaccination campaign onset Canadian Journal of Infectious Diseases and Medical Microbiology Clinic staff perceptions of data collection methodologies used during the H1N1 influenza campaign in Canada Canadian Journal of Infectious Diseases and Medical Microbiology Approaches to immunization data collection employed across Canada during the H1N1 vaccination campaign Canadian Journal of Infectious Diseases and Medical Microbiology Time and motion study to compare electronic and hybrid data collection systems during the pandemic H1N1 influenza vaccination campaign Canadian Journal of Infectious Diseases and Medical Microbiology Acceptability of web-based influenza immunization adverse event self-reporting among health care workers American Journal of Epidemiology Rotavirus testing and admitting practices in 12 pediatric hospitals: implications for surveillance American Journal of Epidemiology Hospital acquired rotavirus infections: substantial disease burden in Canadian Pediatric hospitals Child: care, health, and development Scheifele and Julie A.

Influenza vaccination in paediatric nurses: Cross-sectional study of coverage, refusal, and factors in acceptance Vaccine Seamus P. Norton and David W. Bettinger and Robert M. West DOI: Burgess and Aaron F. Hirschfeld and Gregory J. Tyrrell and Julie A. Bettinger and Stuart E. Turvey DOI: Scheifele and Julie Bettinger and David M. Tyrrell DOI: Prevalence of Toll-like receptor signalling defects in apparently healthy children who developed invasive pneumococcal infection Clinical Immunology Aaron F.

Hirschfeld and Julie A. Bettinger and Rachel E. Victor and Donald J. Davidson and Andrew J. Currie and J. Mark Ansermino and David W. Scheifele and Jordan S. Orange and Stuart E. Bettinger and Phyllis E. Kozarsky DOI: Morbidity and mortality of invasive meningococcal disease in Canada Canadian Journal of Infectious Diseases and Medical Microbiology Effectiveness of influenza vaccine in preventing hospitalization of children months of age Canadian Journal of Infectious Diseases and Medical Microbiology Bettinger and Nancy E.

Adler and Frank C. Curriero and Jonathan M. Ellen DOI: Bettinger and David D. Celentano and Frank C. Curriero and Nancy E. Adler and Susan G. Millstein and Jonathan M. Update: outbreak of acute febrile illness among athletes participating in eco-challenge Sabah Morbidity Mortality Weekly Report Appropriately vaccinated children among hospitalized pertussis cases Canadian Journal of Infectious Diseases and Medical Microbiology.

Does deep kissing increase the risk of meningitis in teens? Parkhurst Exchange. Current Projects Population based active surveillance for meningococcal infections in adults and children across Canada. Current Projects Canada National Vaccine Safety network: monitors the safety of seasonal influenza vaccine each year. It can take years to turn scientific breakthrough into new interventions and treatments.

Funding helps speed the pace of change. Sign up for compelling stories about innovative science, the doctors and researchers who help turn ideas into discoveries and treatments, and the kids and families whose lives are changed.

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